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05 Data Science Environment Setup/038 Windows Environment Setup 2.mp4 238.7 MB
09 Scikit-learn Creating Machine Learning Models/101 Typical scikit-learn Workflow.mp4 199.4 MB
09 Scikit-learn Creating Machine Learning Models/102 Optional Debugging Warnings In Jupyter.mp4 184.7 MB
16 Career Advice Extra Bits/257 CWD Git Github.mp4 184.7 MB
09 Scikit-learn Creating Machine Learning Models/133 Tuning Hyperparameters.mp4 184.1 MB
14 Neural Networks Deep Learning Transfer Learning and TensorFlow 2/228 Training Your Deep Neural Network.mp4 174.7 MB
16 Career Advice Extra Bits/251 What If I Dont Have Enough Experience.mp4 168.8 MB
12 Milestone Project 2 Supervised Learning (Time Series Data)/170 Feature Engineering.mp4 166.9 MB
09 Scikit-learn Creating Machine Learning Models/139 Putting It All Together.mp4 166.0 MB
14 Neural Networks Deep Learning Transfer Learning and TensorFlow 2/230 Make And Transform Predictions.mp4 162.5 MB
14 Neural Networks Deep Learning Transfer Learning and TensorFlow 2/217 Turning Data Into Batches 2.mp4 156.6 MB
12 Milestone Project 2 Supervised Learning (Time Series Data)/171 Turning Data Into Numbers.mp4 153.3 MB
05 Data Science Environment Setup/035 Mac Environment Setup.mp4 151.4 MB
14 Neural Networks Deep Learning Transfer Learning and TensorFlow 2/233 Visualizing And Evaluate Model Predictions 2.mp4 150.8 MB
09 Scikit-learn Creating Machine Learning Models/109 Choosing The Right Model For Your Data.mp4 150.2 MB
12 Milestone Project 2 Supervised Learning (Time Series Data)/183 Feature Importance.mp4 149.2 MB
14 Neural Networks Deep Learning Transfer Learning and TensorFlow 2/237 Making Predictions On Test Images.mp4 147.7 MB
14 Neural Networks Deep Learning Transfer Learning and TensorFlow 2/236 Training Model On Full Dataset.mp4 146.6 MB
12 Milestone Project 2 Supervised Learning (Time Series Data)/181 Preproccessing Our Data.mp4 146.1 MB
11 Milestone Project 1 Supervised Learning (Classification)/151 Finding Patterns 3.mp4 144.6 MB

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【影视】 [Tutorialsplanet.NET] Udemy - Feature Engineering for Machine Learning

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13 - Assembling a feature engineering pipeline/004 Regression pipeline.mp4 106.0 MB
06 - Categorical Variable Encoding/017 Weight of Evidence Demo.mp4 103.1 MB
04 - Missing Data Imputation/008 Random sample imputation.mp4 91.9 MB
06 - Categorical Variable Encoding/004 One-hot-encoding Demo.mp4 90.1 MB
13 - Assembling a feature engineering pipeline/003 Classification pipeline.mp4 80.3 MB
06 - Categorical Variable Encoding/018 Comparison of categorical variable encoding.mp4 79.9 MB
08 - Discretisation/012 Discretisation with decision trees using Scikit-learn.mp4 79.2 MB
08 - Discretisation/004 Equal-width discretisation Demo.mp4 71.5 MB
06 - Categorical Variable Encoding/012 Target guided ordinal encoding Demo.mp4 69.1 MB
04 - Missing Data Imputation/016 Automatic determination of imputation method with Sklearn.mp4 68.6 MB
06 - Categorical Variable Encoding/020 Rare label encoding Demo.mp4 63.5 MB
13 - Assembling a feature engineering pipeline/005 Feature engineering pipeline with cross-validation.mp4 56.8 MB
06 - Categorical Variable Encoding/006 One hot encoding of top categories Demo.mp4 56.5 MB
01 - Introduction/001 Course curriculum overview.mp4 52.0 MB
06 - Categorical Variable Encoding/008 Ordinal encoding Demo.mp4 51.9 MB
10 - Feature Scaling/013 Scaling to vector unit length Demo.mp4 47.0 MB
07 - Variable Transformation/003 Variable Transformation with Scikit-learn.mp4 46.7 MB
10 - Feature Scaling/005 Mean normalisation Demo.mp4 45.2 MB
07 - Variable Transformation/002 Variable Transformation with Numpy and SciPy.mp4 44.5 MB
03 - Variable Characteristics/005 Linear models assumptions.mp4 43.5 MB

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【影视】 Learning the Cisco Application-Centric Infrastructure (ACI)

收录时间:2025-03-16 文档个数:84 文档大小:2.2 GB 最近下载:2025-05-16 人气:905 磁力链接

Chapter 6 Secured External Connectivity with L3OUTs/001. External L3OUT with BGP.mp4 102.0 MB
Chapter 6 Secured External Connectivity with L3OUTs/003. Tenants to L3OUT Part One.mp4 90.2 MB
Chapter 8 Automation and Orchestration with ACI/003. Bulk ACI Tenant Automation using Python Loops.mp4 82.9 MB
Chapter 7 Layer 2 Through Layer 7 Service Insertion/001. External L2OUT.mp4 82.0 MB
Chapter 1 Introduction to Application Centric Infrastructure (ACI)/002. Spine and Leaf Architecture.mp4 81.8 MB
Chapter 6 Secured External Connectivity with L3OUTs/002. External L3OUT with OSPF.mp4 79.6 MB
Chapter 4 Multi-Tenancy ACI's Ultimate Weapon/004. ACI Domains to Tenant Relationship.mp4 78.2 MB
Chapter 7 Layer 2 Through Layer 7 Service Insertion/003. DHCP Services In ACI.mp4 76.6 MB
Chapter 7 Layer 2 Through Layer 7 Service Insertion/005. Adding Routed Firewalls in ACI.mp4 75.5 MB
Chapter 3 Enabling Connectivity in ACI/006. Virtual Machine Manager Integration in ACI.mp4 74.5 MB
Chapter 2 Let's Bring the ACI Fabric to Life/004. ACI APIC Web User Interface.mp4 71.3 MB
Chapter 1 Introduction to Application Centric Infrastructure (ACI)/003. ACI Fabric Components.mp4 68.4 MB
Chapter 5 Secured Internal Connectivity In ACI/003. Inter and Intra Tenant Communications and Contract Interfaces.mp4 66.8 MB
Chapter 6 Secured External Connectivity with L3OUTs/004. Tenants to L3OUT Part Two.mp4 66.3 MB
Chapter 8 Automation and Orchestration with ACI/001. Rest API with ACI Toolkit.mp4 62.8 MB
Chapter 4 Multi-Tenancy ACI's Ultimate Weapon/002. Application Profiles and Endpoint Groups.mp4 60.1 MB
Chapter 3 Enabling Connectivity in ACI/001. Interface Policy Groups, Profiles, and Policies.mp4 59.8 MB
Chapter 4 Multi-Tenancy ACI's Ultimate Weapon/003. Subjects, Filters, and Contracts.mp4 59.0 MB
Chapter 3 Enabling Connectivity in ACI/003. Fabric Domains and VLAN Pools.mp4 58.0 MB
Chapter 7 Layer 2 Through Layer 7 Service Insertion/002. Vlan Stitching.mp4 57.8 MB

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【影视】 [OneHack.Us] Coursera - Practical Deep Learning With Python 2025

收录时间:2025-03-13 文档个数:94 文档大小:2.9 GB 最近下载:2025-05-15 人气:2604 磁力链接

03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization/02-Lstm_Architecture/09-demonstration_next_word_prediction_model_compilation_and_prediction.mp4 101.3 MB
01-Deep_Learning_Components/03-Building_Perceptron_And_Its_Working/09-demonstration_handwritten_digits_classification_optimizing_the_model.mp4 93.1 MB
02-Deep_Learning_With_Cnn_Rcnn_And_Faster_Rcnn/03-Faster_Rcnn_Recurrent_Convolutional_Neural_Network/05-demonstration_object_detection_with_faster_rcnn_building_the_model.mp4 86.9 MB
02-Deep_Learning_With_Cnn_Rcnn_And_Faster_Rcnn/01-Convolutional_Neural_Network/11-demonstration_building_basic_cnn_model_with_new_parameters.mp4 82.0 MB
01-Deep_Learning_Components/02-Essentials_Of_Deep_Learning/13-single_layer_perceptron_decision_boundary.mp4 80.9 MB
02-Deep_Learning_With_Cnn_Rcnn_And_Faster_Rcnn/03-Faster_Rcnn_Recurrent_Convolutional_Neural_Network/04-demonstration_object_detection_with_faster_rcnn_pretrained_model_setup.mp4 78.3 MB
01-Deep_Learning_Components/03-Building_Perceptron_And_Its_Working/08-demonstration_handwritten_digits_classification_designing_the_model.mp4 76.8 MB
02-Deep_Learning_With_Cnn_Rcnn_And_Faster_Rcnn/01-Convolutional_Neural_Network/10-demonstration_adding_more_layers.mp4 65.4 MB
03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization/01-Working_Of_Recurrent_Neural_Networks_Rnn/06-demonstration_rnn_building_the_model.mp4 65.4 MB
03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization/01-Working_Of_Recurrent_Neural_Networks_Rnn/05-demonstration_rnn_dataset_preparation.mp4 65.1 MB
03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization/02-Lstm_Architecture/08-demonstration_next_word_prediction_layers.mp4 61.8 MB
03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization/03-Module_Optimization_And_Compilation/06-demonstration_model_compilation_preparing_the_dataset.mp4 58.2 MB
02-Deep_Learning_With_Cnn_Rcnn_And_Faster_Rcnn/02-Tensorflow_Hub_For_Object_Detection_Using_Faster_Rcnn/07-demonstration_training_the_model_and_visualizing_the_predictions.mp4 56.2 MB
02-Deep_Learning_With_Cnn_Rcnn_And_Faster_Rcnn/01-Convolutional_Neural_Network/07-demonstration_designing_the_model.mp4 55.4 MB
03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization/02-Lstm_Architecture/07-demonstration_next_word_prediction_processing_the_corpus.mp4 52.6 MB
03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization/03-Module_Optimization_And_Compilation/07-demonstration_building_and_compiling_model.mp4 48.5 MB
03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization/03-Module_Optimization_And_Compilation/08-demonstration_from_rmsprop_to_adam.mp4 47.4 MB
01-Deep_Learning_Components/02-Essentials_Of_Deep_Learning/12-single_layer_perceptron_define_sigmoid_function.mp4 46.1 MB
01-Deep_Learning_Components/02-Essentials_Of_Deep_Learning/03-neural_networks.mp4 44.2 MB
02-Deep_Learning_With_Cnn_Rcnn_And_Faster_Rcnn/01-Convolutional_Neural_Network/06-demonstration_load_and_preprocess_the_data.mp4 44.1 MB

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【影视】 LinkedIn Learning - Learning Full-Stack JavaScript Development MongoDB, Node, and React [CoursesGhar]

收录时间:2025-03-13 文档个数:69 文档大小:371.8 MB 最近下载:2025-05-16 人气:94 磁力链接

[8] 7. Working with MongoDB [CoursesGhar.Com]/[9] Alternatives MERN and Electrode.mp4 27.4 MB
[8] 7. Working with MongoDB [CoursesGhar.Com]/[3] Displaying the list of names.mp4 25.2 MB
[8] 7. Working with MongoDB [CoursesGhar.Com]/[1] Reading data from MongoDB.mp4 23.4 MB
[8] 7. Working with MongoDB [CoursesGhar.Com]/[6] Creating an API to propose a name.mp4 23.3 MB
[8] 7. Working with MongoDB [CoursesGhar.Com]/[7] Wiring the proposed new name form.mp4 23.0 MB
[2] 1. Getting Started [CoursesGhar.Com]/[2] Setting up project dependencies.mp4 17.4 MB
[4] 3. React Basics [CoursesGhar.Com]/[1] React elements and JSX.mp4 17.0 MB
[6] 5. Rendering on the Server [CoursesGhar.Com]/[3] Fix the checksum problem.mp4 15.7 MB
[2] 1. Getting Started [CoursesGhar.Com]/[1] Modern JavaScript.mp4 14.2 MB
[8] 7. Working with MongoDB [CoursesGhar.Com]/[4] Example script to update all data.mp4 11.7 MB
[3] 2. Node as a Web Server [CoursesGhar.Com]/[4] Using the EJS template language.mp4 11.1 MB
[5] 4. Working with Data [CoursesGhar.Com]/[3] Using Sass with Node.mp4 11.1 MB
[7] 6. Routing on Client and Server [CoursesGhar.Com]/[2] Navigating to a contest.mp4 11.0 MB
[3] 2. Node as a Web Server [CoursesGhar.Com]/[3] Creating an Express server.mp4 10.9 MB
[2] 1. Getting Started [CoursesGhar.Com]/[3] Project structure and configurations.mp4 10.8 MB
[7] 6. Routing on Client and Server [CoursesGhar.Com]/[5] A bit of refactoring.mp4 10.8 MB
[5] 4. Working with Data [CoursesGhar.Com]/[5] Fetching data from a remote API.mp4 9.7 MB
[4] 3. React Basics [CoursesGhar.Com]/[2] React components.mp4 9.6 MB
[3] 2. Node as a Web Server [CoursesGhar.Com]/[1] Node modules.mp4 9.5 MB
[4] 3. React Basics [CoursesGhar.Com]/[5] Component state.mp4 9.5 MB

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【影视】 [ WebToolTip.com ] Udemy - Data Science And Machine Learning With R - A Complete Masterc

收录时间:2025-03-13 文档个数:77 文档大小:2.1 GB 最近下载:2025-05-16 人气:1088 磁力链接

~Get Your Files Here !/2 - What is data science.mp4 87.0 MB
~Get Your Files Here !/50 - Json parsing.mp4 74.1 MB
~Get Your Files Here !/64 - Data preprocessing.mp4 73.0 MB
~Get Your Files Here !/7 - Getting started with r.mp4 71.7 MB
~Get Your Files Here !/68 - Hands on exploratory data analysis.mp4 65.6 MB
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~Get Your Files Here !/27 - Intermediate r section intro.mp4 46.6 MB
~Get Your Files Here !/63 - Data preprocessing Intro.mp4 46.5 MB
~Get Your Files Here !/39 - Data manipulation in r section intro.mp4 46.2 MB
~Get Your Files Here !/71 - Logistic regression intro.mp4 46.1 MB
~Get Your Files Here !/51 - Data visualization in r intro.mp4 46.1 MB
~Get Your Files Here !/61 - Intro to machine learning Part 1.mp4 45.9 MB
~Get Your Files Here !/69 - Linear regression a real model intro.mp4 45.7 MB
~Get Your Files Here !/62 - Intro to machine learning Part 2.mp4 45.5 MB
~Get Your Files Here !/65 - Linear regression a simple model Intro.mp4 45.4 MB
~Get Your Files Here !/3 - Machine learning overview.mp4 40.8 MB
~Get Your Files Here !/58 - Intro to r shiny.mp4 37.8 MB
~Get Your Files Here !/67 - Exploratory data analysis intro.mp4 37.7 MB
~Get Your Files Here !/48 - String manipulation.mp4 35.0 MB

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【影视】 [OneHack.Us] Edureka.co - Applied Machine Learning With Python 2025

收录时间:2025-03-10 文档个数:101 文档大小:3.9 GB 最近下载:2025-05-15 人气:1750 磁力链接

03-Association_Rule_Mining_And_Recommendation_System/01-Association_Rules/03-demonstrating_apriori_algorithm.mp4 91.9 MB
02-Machine_Learning_Algorithms/02-Svm_Knn_And_Naive_Bayes_Algorithms/02-terminologies_in_svm.mp4 90.4 MB
02-Machine_Learning_Algorithms/02-Svm_Knn_And_Naive_Bayes_Algorithms/03-hinge_loss_function_and_other_parameters.mp4 86.8 MB
02-Machine_Learning_Algorithms/01-Decision_Tree_And_Random_Forest/04-step_by_step_building_of_decision_tree.mp4 81.6 MB
01-Introduction_To_Machine_Learning/03-Regression/03-calculating_ols.mp4 79.1 MB
02-Machine_Learning_Algorithms/02-Svm_Knn_And_Naive_Bayes_Algorithms/08-example_of_naive_bayes_algorithm.mp4 78.5 MB
03-Association_Rule_Mining_And_Recommendation_System/03-Reinforcement_Learning_And_Boosting/10-demonstration_of_boosting_building_a_model.mp4 76.8 MB
02-Machine_Learning_Algorithms/02-Svm_Knn_And_Naive_Bayes_Algorithms/05-demonstration_of_svm_setting_up_the_svm_classifier.mp4 71.3 MB
02-Machine_Learning_Algorithms/01-Decision_Tree_And_Random_Forest/07-demonstration_building_decision_tree_and_random_forest.mp4 66.7 MB
02-Machine_Learning_Algorithms/01-Decision_Tree_And_Random_Forest/02-what_is_decision_tree.mp4 66.7 MB
03-Association_Rule_Mining_And_Recommendation_System/03-Reinforcement_Learning_And_Boosting/12-demonstration_of_k_means_clustering.mp4 64.1 MB
02-Machine_Learning_Algorithms/03-Dimensionality_Reduction/04-eigen_values_and_eigen_vectors.mp4 62.3 MB
01-Introduction_To_Machine_Learning/04-Evaluation_Metrics/02-example_for_calculating_confusion_matrix.mp4 61.4 MB
02-Machine_Learning_Algorithms/03-Dimensionality_Reduction/09-demonstration_of_lda.mp4 61.4 MB
01-Introduction_To_Machine_Learning/04-Evaluation_Metrics/04-overfitting_and_underfitting.mp4 60.3 MB
02-Machine_Learning_Algorithms/03-Dimensionality_Reduction/01-dimensionality_reduction.mp4 60.2 MB
01-Introduction_To_Machine_Learning/03-Regression/14-demonstration_of_logistic_regression_transforming_data.mp4 60.1 MB
03-Association_Rule_Mining_And_Recommendation_System/02-Recommendation_Engines/03-demonstration_of_recommendation_engine_preparing_data.mp4 59.5 MB
03-Association_Rule_Mining_And_Recommendation_System/03-Reinforcement_Learning_And_Boosting/03-demonstration_of_boosting_cleaning_and_transforming_dataset.mp4 59.0 MB
02-Machine_Learning_Algorithms/03-Dimensionality_Reduction/08-working_of_lda.mp4 58.2 MB

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【影视】 [Artstation Learning] Creating a Subsurface Scatter Material in Substance Designer [ENG-RUS]

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【影视】 [FreeCourseSite.com] Udemy - Deep Learning A-Z 2025 Neural Networks, AI & ChatGPT Prize

收录时间:2025-03-08 文档个数:457 文档大小:6.6 GB 最近下载:2025-05-16 人气:739 磁力链接

06. CNN Intuition/8. How Do Fully Connected Layers Work in Convolutional Neural Networks (CNNs).mp4 192.2 MB
07. Building a CNN/7. Develop an Image Recognition System Using Convolutional Neural Networks.mp4 161.6 MB
09. RNN Intuition/6. How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4 159.4 MB
18. Building a Boltzmann Machine/16. Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4 151.1 MB
17. Boltzmann Machine Intuition/5. How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4 149.4 MB
13. SOMs Intuition/8. Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4 132.4 MB
10. Building a RNN/14. Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4 132.0 MB
07. Building a CNN/3. Step 2 - Deep Learning Preprocessing Scaling & Transforming Images for CNNs.mp4 115.2 MB
09. RNN Intuition/3. What is a Recurrent Neural Network (RNN) Deep Learning for Sequential Data.mp4 113.5 MB
21. Building an AutoEncoder/7. Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4 109.1 MB
10. Building a RNN/5. Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.mp4 108.8 MB
18. Building a Boltzmann Machine/7. Step 4 - Convert Training & Test Sets to RBM-Ready Arrays in Python.mp4 108.3 MB
18. Building a Boltzmann Machine/13. Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4 104.1 MB
21. Building an AutoEncoder/3. Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4 103.9 MB
13. SOMs Intuition/5. How Self-Organizing Maps (SOMs) Learn Unsupervised Deep Learning Explained.mp4 103.8 MB
06. CNN Intuition/6. Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4 94.5 MB
17. Boltzmann Machine Intuition/2. Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.mp4 91.6 MB
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